Given the presumed importance of benthic and epibenthic estuarine habitats in Chinook salmon (Oncorhynchus tshawytscha) smolt growth and survival, resource managers would be well served by an improved understanding of how smolts use such habitats. A cabled acoustic positioning system was used to precisely track (<1 m resolution) the movement of seventeen 0-aged hatchery-reared fall Chinook smolts in a large (similar to 400 m(2)) enclosure over a period of 10 days in Willapa Bay, Washington, USA. A hierarchical Bayesian state-space model of movement was subsequently developed to associate the behaviors of tagged salmon with characteristics of benthic habitat in the enclosure. Model results indicated that smolts had a strong preference for remaining in native eelgrass (Zostera marina). Conversely, no such preference existed for other structured benthic habitats such as oyster (Crassostrea gigas) beds, non-native eelgrass (Zostera japonica), and non-native smooth crodgrass (Spartina alterniflora). There was a positive relationships between individual survivorship in the enclosure and the strength of behavioral preference for native eelgrass, suggesting that predator avoidance may be the evolutionary mechanism driving behavioral responses of smolts to benthic habitats.

Myriad human activities increasingly threaten the existence of many species. A variety of conservation interventions such as habitat restoration, protected areas, and captive breeding have been used to prevent extinctions. Evaluating the effectiveness of these interventions requires appropriate statistical methods, given the quantity and quality of available data. Historically, analysis of variance has been used with some form of predetermined before-after control-impact design to estimate the effects of large-scale experiments or conservation interventions. However, ad hoc retrospective study designs or the presence of random effects at multiple scales may preclude the use of these tools. We evaluated the effects of a large-scale supplementation program on the density of adult Chinook salmon Oncorhynchus tshawytscha from the Snake River basin in the northwestern United States currently listed under the U.S. Endangered Species Act. We analyzed 43years of data from 22 populations, accounting for random effects across time and space using a form of Bayesian hierarchical time-series model common in analyses of financial markets. We found that varying degrees of supplementation over a period of 25years increased the density of natural-origin adults, on average, by 0-8% relative to nonsupplementation years. Thirty-nine of the 43year effects were at least two times larger in magnitude than the mean supplementation effect, suggesting common environmental variables play a more important role in driving interannual variability in adult density. Additional residual variation in density varied considerably across the region, but there was no systematic difference between supplemented and reference populations. Our results demonstrate the power of hierarchical Bayesian models to detect the diffuse effects of management interventions and to quantitatively describe the variability of intervention success. Nevertheless, our study could not address whether ecological factors (e.g., competition) were more important than genetic considerations (e.g., inbreeding depression) in determining the response to supplementation.

The ongoing evolution of tracer mixing models has resulted in a confusing array of software tools that differ in terms of data inputs, model assumptions, and associated analytic products. Here we introduce MixSIAR, an inclusive, rich, and flexible Bayesian tracer (e.g., stable isotope) mixing model framework implemented as an open-source R package. Using MixSIAR as a foundation, we provide guidance for the implementation of mixing model analyses. We begin by outlining the practical differences between mixture data error structure formulations and relate these error structures to common mixing model study designs in ecology. Because Bayesian mixing models afford the option to specify informative priors on source proportion contributions, we outline methods for establishing prior distributions and discuss the influence of prior specification on model outputs. We also discuss the options available for source data inputs (raw data versus summary statistics) and provide guidance for combining sources. We then describe a key advantage of MixSIAR over previous mixing model software-the ability to include fixed and random effects as covariates explaining variability in mixture proportions and calculate relative support for multiple models via information criteria. We present a case study of Alligator mississippiensis diet partitioning to demonstrate the power of this approach. Finally, we conclude with a discussion of limitations to mixing model applications. Through MixSIAR, we have consolidated the disparate array of mixing model tools into a single platform, diversified the set of available parameterizations, and provided developers a platform upon which to continue improving mixing model analyses in the future.

Large predators are often highly mobile and can traverse and use multiple habitats. We know surprisingly little about how predator mobility determines important processes of ecosystem connectivity. Here we used a variety of data sources drawn from Palmyra Atoll, a remote tropical marine ecosystem where large predators remain in high abundance, to investigate how these animals foster connectivity. Our results indicate that three of Palmyra's most abundant large predators (e.g., two reef sharks and one snapper) use resources from different habitats creating important linkages across ecosystems. Observations of cross-system foraging such as this have important implications for the understanding of ecosystem functioning, the management of large-predator populations, and the design of conservation measures intended to protect whole ecosystems. In the face of widespread declines of large, mobile predators, it is important that resource managers, policy makers, and ecologists work to understand how these predators create connectivity and to determine the impact that their depletions may be having on the integrity of these linkages.

In this paper, we review recent advances in stable isotope mixing models (SIMMs) and place them into an overarching Bayesian statistical framework, which allows for several useful extensions. SIMMs are used to quantify the proportional contributions of various sources to a mixture. The most widely used application is quantifying the diet of organisms based on the food sources they have been observed to consume. At the centre of the multivariate statistical model we propose is a compositional mixture of the food sources corrected for various metabolic factors. The compositional component of our model is based on the isometric log-ratio transform. Through this transform, we can apply a range of time series and non-parametric smoothing relationships. We illustrate our models with three case studies based on real animal dietary behaviour. Copyright (c) 2013 John Wiley & Sons, Ltd.

Stable isotope mixing models are increasingly used to quantify consumer diets, but may be misused and misinterpreted. We address major challenges to their effective application. Mixing models have increased rapidly in sophistication. Current models estimate probability distributions of source contributions, have user-friendly interfaces, and incorporate complexities such as variability in isotope signatures, discrimination factors, hierarchical variance structure, covariates, and concentration dependence. For proper implementation of mixing models, we offer the following suggestions. First, mixing models can only be as good as the study and data. Studies should have clear questions, be informed by knowledge of the system, and have strong sampling designs to effectively characterize isotope variability of consumers and resources on proper spatio-temporal scales. Second, studies should use models appropriate for the question and recognize their assumptions and limitations. Decisions about source grouping or incorporation of concentration dependence can influence results. Third, studies should be careful about interpretation of model outputs. Mixing models generally estimate proportions of assimilated resources with substantial uncertainty distributions. Last, common sense, such as graphing data before analyzing, is essential to maximize usefulness of these tools. We hope these suggestions for effective implementation of stable isotope mixing models will aid continued development and application of this field.

The destructiveness of major (Category 3 to 5) hurricanes along the United States Atlantic Ocean seaboard has been recognized for centuries. While the effects of hurricanes on coastal ecosystems are well known, the influence of hurricanes on pelagic seabirds is difficult to assess. During the annual Atlantic hurricane season (similar to 1 June to 30 November), the endangered black-capped petrel Pterodroma hasitata aggregates in Gulf Stream habitats from Florida to North Carolina. On at least 8 occasions over the past century, hurricanes have driven petrels far inland (sometimes as far as the Great Lakes), suggesting the demise of 10s to 100s of individuals. This paper models >100 yr of data to characterize and compare key aspects of hurricanes that did and did not drive petrels inland. Our model suggests that the predicted increase in the frequency of Category 3 to 5 hurricanes in the region due to climate change could nearly double the expected number of wrecked petrels over the next century and place an endangered species at greater risk of extinction.

There is a global trend in the depletion of transient reef fish spawning aggregations ("FSAs"), making them a primary target for management with marine protected areas (MPAs). Here, we review the observed and likely effectiveness of FSA MPAs, discuss how future studies could fill knowledge gaps, and provide recommendations for MPA design based on species' life history and behaviour, enforcement potential, and management goals. Modelling studies indicate that FSA MPAs can increase spawning-stock biomass and normalize sex ratio in protogynous fish populations, unless fishing mortality remains high outside protected FSA sites and spawning times. In the field, observations of no change or continued decline in spawning biomass are more common than population recovery. When empirical studies suggest that FSA MPAs may not benefit fish productivity or recovery, extenuating factors such as insufficient time since MPA creation, poor or lack of enforcement, inadequate design, and poorly defined management objectives are generally blamed rather than failure of the MPA concept. Results from both the empirical and modelling literature indicate that FSA MPAs may not improve exploitable biomass and fisheries yields; however, investigations are currently too limited to draw conclusions on this point. To implement effective FSA MPAs, additional modelling work, long-term monitoring programmes at FSA sites, and collections of fisheries-dependent data are required, with greater attention paid to the design and enforcement of area closures. We recommend a harmonized, adaptive approach that combines FSA MPA design with additional management measures to achieve explicitly stated objectives. Conservation objectives and, therefore, an overall reduction in mortality rates should be targeted first. Fisheries objectives build on conservation objectives, in that they require an overall reduction in mortality rates while maintaining sufficient access to exploitable biomass. Communication among researchers, regulatory agencies, park authorities, and fishers will be paramount for effective action, along with significant funds for implementation and enforcement.

The REEF Fish Survey Project is a volunteer fish monitoring program developed by the Reef Environmental Education Foundation ( REEF). REEF volunteers collect fish distribution and abundance data using a standardized visual method during regular diving and snorkeling activities. Survey data are recorded on preprinted data sheets that are returned to REEF and optically digitized. Data are housed in a publicly accessible database on REEF's Web site (http:// www. reef. org). Since the project's inception in 1993, over 40,000 surveys have been conducted in the coastal waters of North America, tropical western Atlantic, Gulf of California and Hawaii. The Fish Survey Project has been incorporated into existing monitoring programs through partnerships with government agencies, scientists, conservation organizations, and private institutions. REEF's partners benefit from the educational value and increased stewardship resulting from volunteer data collection. Applications of the data include an evaluation of fish/habitat interactions in the Florida Keys National Marine Sanctuary, the development of a multi-species trend analysis method to identify sites of management concern, assessment of the current distribution of species, status reports on fish assemblages of marine parks, and the evaluation of no-take zones in the Florida Keys. REEF's collaboration with a variety of partners, combined with the Fish Survey Project's standardized census method and database management system, has resulted in a successful citizen science monitoring program.

Increasing complexity in human-environment interactions at multiple watershed scales presents major challenges to sediment source apportionment data acquisition and analysis. Herein, we present a step-change in the application of Bayesian mixing models: Deconvolutional-MixSIAR (D-MIXSIAR) to underpin sustainable management of soil and sediment. This new mixing model approach allows users to directly account for the 'structural hierarchy' of a river basin in terms of sub-watershed distribution. It works by deconvoluting apportionment data derived for multiple nodes along the stream-river network where sources are stratified by sub-watershed. Source and mixture samples were collected from two watersheds that represented (i) a longitudinal mixed agricultural watershed in the south west of England which had a distinct upper and lower zone related to topography and (ii) a distributed mixed agricultural and forested watershed in the mid-hills of Nepal with two distinct sub-watersheds. In the former, geochemical fingerprints were based upon weathering profiles and anthropogenic soil amendments. In the latter compound-specific stable isotope markers based on soil vegetation cover were applied. Mixing model posterior distributions of proportional sediment source contributions differed when sources were pooled across the watersheds (pooled-MixSIAR) compared to those where source terms were stratified by sub-watershed and the outputs deconvoluted (D-MixSIAR). In the first example, the stratified source data and the deconvolutional approach provided greater distinction between pasture and cultivated topsoil source signatures resulting in a different posterior distribution to non-deconvolutional model (conventional approaches over-estimated the contribution of cultivated land to downstream sediment by 2 to 5 times). In the second example, the deconvolutional model elucidated a large input of sediment delivered from a small tributary resulting in differences in the reported contribution of a discrete mixed forest source. Overall D-MixSIAR model posterior distributions had lower (by ca 25-50%) uncertainty and quicker model run times. In both cases, the structured, deconvoluted output cohered more closely with field observations and local knowledge underpinning the need for closer attention to hierarchy in source and mixture terms in river basin source apportionment. Soil erosion and siltation challenge the energy-food-water-environment nexus. This new tool for source apportionment offers wider application across complex environmental systems affected by natural and human-induced change and the lessons learned are relevant to source apportionment applications in other disciplines.

Managing natural populations and communities requires detailed information regarding demographic processes at large spatial and temporal scales. This combination is challenging for both traditional scientific surveys, which often operate at localized scales, and recent citizen science designs, which often provide data with few auxiliary information (i.e., no information about individual age or condition). We therefore combine citizen science data at large scales with the demographic resolution afforded by recently developed, site-structured demographic models. We apply this approach to categorical data generated from citizen science representing species density of two managed reef fishes in the Gulf of Mexico, and use a modified Dail-Madsen model to estimate demographic trends, habitat associations, and interannual variability in recruitment. This approach identifies strong preferences for artificial structure for the recovering Goliath grouper, while revealing little evidence of either habitat associations or trends in abundance for mutton snapper. Results are also contrasted with a typical generalized linear mixed-model (GLMM) approach, using real-world and simulated data, to demonstrate the importance of accounting for the statistical complexities implied by spatially structured citizen science data. We conclude by discussing the increasing potential for synthesizing demographic models and citizen science data, and the management benefits that can be accrued.

Given the rapid population decline and recent petition for listing of the monarch butterfly (Danaus plexippus L.) under the Endangered Species Act, an accurate estimate of the Eastern, migratory population size is needed. Because of difficulty in counting individual monarchs, the number of hectares occupied by monarchs in the overwintering area is commonly used as a proxy for population size, which is then multiplied by the density of individuals per hectare to estimate population size. There is, however, considerable variation in published estimates of overwintering density, ranging from 6.9-60.9 million ha(-1). We develop a probability distribution for overwinter density of monarch butterflies from six published density estimates. The mean density among the mixture of the six published estimates was similar to 27.9 million butterflies ha(-1) (95% CI [2.4-80.7] million ha(-1)); the mixture distribution is approximately log-normal, and as such is better represented by the median (21.1 million butterflies ha(-1)). Based upon assumptions regarding the number of milkweed needed to support monarchs, the amount of milkweed (Asciepias spp.) lost (0.86 billion stems) in the northern US plus the amount of milkweed remaining (1.34 billion stems), we estimate >1.8 billion stems is needed to return monarchs to an average population size of 6 ha. Considerable uncertainty exists in this required amount of milkweed because of the considerable uncertainty occurring in overwinter density estimates. Nevertheless, the estimate is on the same order as other published estimates, The studies included in our synthesis differ substantially by year, location, method, and measures of precision. A better understanding of the factors influencing overwintering density across space and time would be valuable for increasing the precision of conservation recommendations.

Many spawning aggregations of marine fishes have been fished beyond the point of sustainability, leading to increased calls for protection through seasonal and/or site-specific fishery closures. Once a closure has been put in place, monitoring the aggregation is imperative in order to learn whether protection leads to the recovery of the population. Current methods for monitoring the status of spawning aggregations rely largely on counts, either subsample or census, usually combined with capturing a subset of the fish to assess individual traits such as length and weight. Handling fish during the spawning aggregation can be stressful for the fish, and can ultimately lead to decreased spawning success, increased susceptibility to predators, or increased mortality through capture trauma or infection. Here we present a novel analysis for monitoring fish on a spawning aggregation that does not require the capture and handling of fish. Following a recovering aggregation of Nassau grouper (Epinephelus striatus) over seven spawning seasons, we show that length-distribution data can be collected by divers using a video-based system with parallel lasers calibrated to a specific distance apart, and subsequently use those data to monitor changes in the size distribution over time. We detected recruitment of new fish to the grouper spawning aggregation in the fourth year of monitoring. In addition to tracking size distribution trends over time, the length distribution information could be combined with an established length-weight regression and an estimate of total abundance to estimate spawning stock biomass. We qualitatively cross-validate this method with census data to evaluate its effectiveness in monitoring the recovery or decline of aggregating species that can be visually observed. (C) 2012 Elsevier Ltd. All rights reserved.

Identifying how social organization shapes individual behavior, survival, and fecundity of animals that live in groups can inform conservation efforts and improve forecasts of population abundance, even when the mechanism responsible for group-level differences is unknown. We constructed a hierarchical Bayesian model to quantify the relative variability in survival rates among different levels of social organization (matrilines and pods) of an endangered population of killer whales (Orcinus orca). Individual killer whales often participate in group activities such as prey sharing and cooperative hunting. The estimated age-specific survival probabilities and survivorship curves differed considerably among pods and to a lesser extent among matrilines (within pods). Across all pods, males had lower life expectancy than females. Differences in survival between pods may be caused by a combination of factors that vary across the population's range, including reduced prey availability, contaminants in prey, and human activity. Our modeling approach could be applied to demographic rates for other species and for parameters other than survival, including reproduction, prey selection, movement, and detection probabilities.

Region-specific conservation programs should have objective, reliable metrics for species prioritization and progress evaluation that are customizable to the goals of a program, easy to comprehend and communicate, and standardized across time. Regional programs may have vastly different goals, spatial coverage, or management agendas, and one-size-fits-all schemes may not always be the best approach. We propose a quantitative and objective framework for generating metrics for prioritizing species that is straightforward to implement and update, customizable to different spatial resolutions, and based on readily available time-series data. This framework is also well-suited to handling missing-data and observer error. We demonstrate this approach using North American Breeding Bird Survey (NABBS) data to identify conservation priority species from a list of over 300 landbirds across 33 bird conservation regions (BCRs). To highlight the flexibility of the framework for different management goals and timeframes we calculate two different metrics. The first identifies species that may be inadequately monitored by NABBS protocols in the near future (TMT, time to monitoring threshold), and the other identifies species likely to decline significantly in the near future based on recent trends (TPD, time to percent decline). Within the individual BCRs we found up to 45% (mean 28%) of the species analyzed had overall declining population trajectories, which could result in up to 37 species declining below a minimum NABBS monitoring threshold in at least one currently occupied BCR within the next 50 years. Additionally, up to 26% (mean 8%) of the species analyzed within the individual BCRs may decline by 30% within the next decade. Conservation workers interested in conserving avian diversity and abundance within these BCRs can use these metrics to plan alternative monitoring schemes or highlight the urgency of those populations experiencing the fastest declines. However, this framework is adaptable to many taxa besides birds where abundance time-series data are available. Published by Elsevier Ltd.

P>While the importance of terrestrial linkages to aquatic ecosystems is well appreciated, the degree of terrestrial support of aquatic consumers remains debated. Estimates of terrestrial contributions to lake zooplankton have omitted a key food source, phytoplankton produced below the mixed layer. We used carbon and nitrogen stable isotope data from 25 Pacific Northwest lakes to assess the relative importance of particulate organic matter (POM) from the mixed layer, below the mixed layer and terrestrial detritus to zooplankton. Zooplankton and deep POM were depleted in 13C relative to mixed layer POM in lakes that can support deep primary production. A Bayesian stable isotope mixing model estimated that terrestrial detritus contributed < 5% to zooplankton production, and confirms the role of lake optical and thermal properties; deep POM accounted for up to 80% of zooplankton production in the clearest lakes. These results suggest terrestrial support of lake zooplankton production is trivial.

Biogeochemical hot moments occur when a temporary increase in availability of one or more limiting reactants results in elevated rates of biogeochemical reactions. Many marine fish form transient spawning aggregations, temporarily increasing their local abundance and thus nutrients supplied via excretion at the aggregation site. In this way, nutrients released by aggregating fish could create a biogeochemical hot moment. Using a combination of empirical and modeling approaches, we estimate nitrogen and phosphorus supplied by aggregating Nassau grouper (Epinephelus striatus). Data suggest aggregating grouper supply up to an order-of-magnitude more nitrogen and phosphorus than daily consumer-derived nutrient supply on coral reefs without aggregating fish. Comparing current and historic aggregation-level excretion estimates shows that overfishing reduced nutrients supplied by aggregating fish by up to 87 %. Our study illustrates a previously unrecognized ecosystem viewpoint regarding fish spawning aggregations and provides an additional perspective on the repercussions of their overexploitation.

Invasions of non-native species in marine ecosystems can be ecologically damaging and economically costly. Identifying 'hot-spots' of non-native species and their sources of introduction is necessary to maximize the effectiveness of invasion quarantine programs. We use a large spatially explicit marine fish database to show that there are a surprising number of non-native fishes on the reefs of southeast Florida, USA. Two likely sources explain the occurrence of non-native marine fishes in this region: introductions through ballast-water exchange, and introductions from aquaria. Data on international shipping patterns and marine fish imports were used to evaluate the culpability of these 2 vectors. Our results suggest that the introductions are the result of aquarium releases. Prevention of further releases and invasions will require education, outreach, and enforcement efforts directed at marine aquarists and the aquarium industry.

Fish spawning aggregations (FSAs) are vital life-history events that need to be monitored to determine the health of aggregating populations; this is especially true of the endangered Nassau grouper (Epinephelus striatus). Hydroacoustics were used to locate Nassau grouper FSAs at sites on the west end of Little Cayman (LCW), and east ends of Grand Cayman (GCE) and Cayman Brac (CBE). Fish abundance and biomass at each FSA were estimated via echo integration and FSA extent. Acoustic mean fish abundance estimates (±SE) on the FSA at LCW (893 ± 459) did not differ significantly from concurrent SCUBA estimates (1150 ± 75). Mean fish densities (number 1000 m−3) were significantly higher at LCW (33.13 ± 5.62) than at the other sites (GCE: 7.01 ± 2.1, CBE: 4.61 ± 1.16). We investigate different acoustic post-processing options to obtain target strength (TS), and we examine the different TS to total length (TL) formulas available. The SCUBA surveys also provided measures of TL through the use of laser callipers allowing development of an in situ TS to TL formula for Nassau grouper at the LCW FSA. Application of this formula revealed mean fish TL was significantly higher at LCW (65.4 ± 0.7 cm) than GCE (60.7 ± 0.4 cm), but not CBE (61.1 ± 2.5 cm). Use of the empirical TS to TL formula resulted in underestimation of fish length in comparison with diver measurements, highlighting the benefits of secondary length data and deriving specific TS to TL formulas for each population. FSA location examined with reference to seasonal marine protected areas (Designated Grouper Spawning Areas) showed FSAs were partially outside these areas at GCE and very close to the boundary at CBE. As FSAs often occur at the limits of safe diving operations, hydroacoustic technology provides an alternative method to monitor and inform future management of aggregating fish species.

The California Commercial Passenger Fishing Vessel (CPFV) fleet is unique in scale of operation, extensive fishing history, and economic impacts. The basses (Paralabrax sp.), which represent a principal target for the CPFV fleet, recently gained more stringent size limits and bag limits. The goal of this study was to conduct a survey of CPFV captains to assess perceptions regarding the status of two Paralabrax species, as well as the impacts of the new regulations. Catch and effort estimates were also obtained using CPFV logbook data to compare captains' perceptions with actual changes in the fishery. The captains agreed that both species are vital to recreational fishing, and that the Barred Sand Bass stock is less healthy than Kelp Bass. Catch and effort analyses were consistent with this perception, with more dramatic declines in CPUE exhibited by Barred Sand Bass. The most experienced captains perceived the status of each species to be in a less healthy state than the less experienced captains, suggesting that shifting baselines are occurring. Most of the captains thought the increased minimum size limits had the greatest short-term impact on the fishing experience. The CPFV logbook data summaries support this assertion, but Kelp Bass CPUE showed a trend reversal. In contrast, Barred Sand Bass CPUE has precipitously declined, and spawning aggregations have been absent since 2013. The agreement between captains' perceptions and logbook analyses strengthens the overall findings, and suggests captains are a valuable resource for informing fisheries management, especially in future studies with data-limited stocks.

We recently described a Bayesian framework for stable isotope mixing models and provided a software tool, MixSIR, for conducting such analyses (Ecol. Lett., 2008; 11:470). Jackson et al. (Ecol. Lett., 2009; 12:E1) criticized the performance of our software based on tests using simulated data. However, their simulation data were flawed, rendering claims of erroneous behaviour inaccurate. A re-evaluation of the MixSIR source code did, however, uncover two minor coding errors, which we have fixed. When data are correctly simulated according to eqns (1)-(4) in Jackson et al. (2009), MixSIR consistently and accurately estimated the proportional contribution of prey to a predator diet, and was surprisingly robust to additional unquantified error. Jackson et al. (2009) also suggested we use a Dirichlet prior on the source proportion parameters, which we agree with. Finally, Jackson et al. (2009) propose adding additional error parameters to our mixing model framework. We caution that such increases in model complexity should be evaluated based on data support.

Stable isotope mixing models offer a statistical framework for estimating the contribution of multiple sources (such as prey) to a mixture distribution. Recent advances in these models have estimated the source proportions using Bayesian methods, but have not explicitly accounted for uncertainty in the mean and variance of sources. We demonstrate that treating these quantities as unknown parameters can reduce bias in the estimated source contributions, although model complexity is increased (thereby increasing the variance of estimates). The advantages of this fully Bayesian approach are particularly apparent when the source geometry is poor or sample sizes are small. A second benefit to treating source quantities as parameters is that prior source information can be included. We present findings from 9 lake food-webs, where the consumer of interest (fish) has a diet composed of 5 sources: aquatic insects, snails, zooplankton, amphipods, and terrestrial insects. We compared the traditional Bayesian stable isotope mixing model with fixed source parameters to our fully Bayesian model with and without an informative prior. The informative prior has much less impact than the choice of model the traditional mixing model with fixed source parameters estimates the diet to be dominated by aquatic insects, while the fully Bayesian model estimates the diet to be more balanced but with greater importance of zooplankton. The findings from this example demonstrate that there can be stark differences in inference between the two model approaches, particularly when the source geometry of the mixing model is poor. These analyses also emphasize the importance of investing substantial effort toward characterizing the variation in the isotopic characteristics of source pools to appropriately quantify uncertainties in their contributions to consumers in food webs.